


from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_SCREAMING_SNAKE_CASE     :					List[Any]      =       logging.get_logger(__name__)

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['input_features', 'attention_mask']
    def __init__(			self     ,   __snake_case=8_0     ,   __snake_case=1_6_0_0_0     ,   __snake_case=0.0     ,   __snake_case=1_0     ,   __snake_case=2_5     ,   __snake_case="hamming_window"     ,   __snake_case=3_2768.0     ,   __snake_case=0.97     ,   __snake_case=1.0     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=False     ,   **__snake_case     ,   ):
         super().__init__(feature_size=__snake_case     ,   sampling_rate=__snake_case     ,   padding_value=__snake_case     ,   **__snake_case       )

         snake_case											=							feature_size
         snake_case											=							sampling_rate
         snake_case											=							padding_value
         snake_case											=							hop_length
         snake_case											=							win_length
         snake_case											=							frame_signal_scale
         snake_case											=							preemphasis_coeff
         snake_case											=							mel_floor
         snake_case											=							normalize_means
         snake_case											=							normalize_vars
         snake_case											=							win_function
         snake_case											=							return_attention_mask

         snake_case											=							win_length * sampling_rate // 1_0_0_0
         snake_case											=							hop_length * sampling_rate // 1_0_0_0

         snake_case											=							optimal_fft_length(self.sample_size       )
         snake_case											=							(self.n_fft // 2) + 1
    def 						a_     (			self     ,   __snake_case       ):
         if self.win_function == "hamming_window":
              snake_case											=							window_function(window_length=self.sample_size     ,   name=self.win_function     ,   periodic=__snake_case       )
         else:
              snake_case											=							window_function(window_length=self.sample_size     ,   name=self.win_function       )

         snake_case											=							mel_filter_bank(
             num_frequency_bins=self.n_freqs     ,   num_mel_filters=self.feature_size     ,   min_frequency=0.0     ,   max_frequency=self.sampling_rate / 2.0     ,   sampling_rate=self.sampling_rate     ,   )

         snake_case											=							spectrogram(
             one_waveform * self.frame_signal_scale     ,   window=__snake_case     ,   frame_length=self.sample_size     ,   hop_length=self.sample_stride     ,   fft_length=self.n_fft     ,   center=__snake_case     ,   preemphasis=self.preemphasis_coeff     ,   mel_filters=__snake_case     ,   mel_floor=self.mel_floor     ,   log_mel='''log'''     ,   )
         return msfc_features.T
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         # make sure we normalize float32 arrays
         if self.normalize_means:
              snake_case											=							x[:input_length].mean(axis=0       )
              snake_case											=							np.subtract(__snake_case     ,   __snake_case       )
         if self.normalize_vars:
              snake_case											=							x[:input_length].std(axis=0       )
              snake_case											=							np.divide(__snake_case     ,   __snake_case       )

         if input_length < x.shape[0]:
              snake_case											=							padding_value

         # make sure array is in float32
         snake_case											=							x.astype(np.floataa       )

         return x
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [self._normalize_one(__snake_case     ,   __snake_case     ,   self.padding_value       ) for x, n in zip(__snake_case     ,   __snake_case       )]




    def __call__(			self     ,   __snake_case     ,   __snake_case = False     ,   __snake_case = None     ,   __snake_case = False     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   **__snake_case     ,   ):

         if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                       F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                       F''' {self.sampling_rate} and not {sampling_rate}.'''       )
         else:
              logger.warning(
                  '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
                  '''Failing to do so can result in silent errors that might be hard to debug.'''       )

         snake_case											=							isinstance(__snake_case     ,   np.ndarray       ) and len(raw_speech.shape       ) > 1
         if is_batched_numpy and len(raw_speech.shape       ) > 2:
              raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''       )
         snake_case											=							is_batched_numpy or (
             isinstance(__snake_case     ,   (list, tuple)       ) and (isinstance(raw_speech[0]     ,   (np.ndarray, tuple, list)       ))
         )

         if is_batched:
              snake_case											=							[np.asarray(__snake_case     ,   dtype=np.floataa       ) for speech in raw_speech]
         elif not is_batched and not isinstance(__snake_case     ,   np.ndarray       ):
              snake_case											=							np.asarray(__snake_case     ,   dtype=np.floataa       )
         elif isinstance(__snake_case     ,   np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
              snake_case											=							raw_speech.astype(np.floataa       )

         # always return batch
         if not is_batched:
              snake_case											=							[raw_speech]

         # extract fbank features
         snake_case											=							[self._extract_mfsc_features(__snake_case       ) for one_waveform in raw_speech]

         # convert into correct format for padding
         snake_case											=							BatchFeature({'''input_features''': features}       )

         snake_case											=							self.pad(
             __snake_case     ,   padding=__snake_case     ,   max_length=__snake_case     ,   truncation=__snake_case     ,   pad_to_multiple_of=__snake_case     ,   return_attention_mask=__snake_case     ,   **__snake_case     ,   )
         # make sure list is in array format
         snake_case											=							padded_inputs.get('''input_features'''       )
         if isinstance(input_features[0]     ,   __snake_case       ):
              snake_case											=							[np.asarray(__snake_case     ,   dtype=np.floataa       ) for feature in input_features]

         snake_case											=							padded_inputs.get('''attention_mask'''       )
         if attention_mask is not None:
              snake_case											=							[np.asarray(__snake_case     ,   dtype=np.intaa       ) for array in attention_mask]

         if self.normalize_means or self.normalize_vars:
              snake_case											=							(
                  np.array(__snake_case     ,   dtype=np.intaa       )
                  if self._get_padding_strategies(__snake_case     ,   max_length=__snake_case       ) is not PaddingStrategy.DO_NOT_PAD
                  and padding
                  else None
              )
              snake_case											=							self.normalize(
                  padded_inputs['''input_features''']     ,   attention_mask=__snake_case       )

         if return_tensors is not None:
              snake_case											=							padded_inputs.convert_to_tensors(__snake_case       )

         return padded_inputs








import requests

_SCREAMING_SNAKE_CASE     :					Optional[int]      =       ""  # <-- Put your OpenWeatherMap appid here!
_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       "https://api.openweathermap.org/data/2.5/"
def      UpperCAmelCase__		(UpperCamelCase_ = "Chicago"				,UpperCamelCase_ = APPID							):



     """simple docstring"""






     return requests.get(URL_BASE + '''weather'''				,params=locals()							).json()
def      UpperCAmelCase__		(UpperCamelCase_ = "Kolkata, India"				,UpperCamelCase_ = APPID							):



     """simple docstring"""






     return requests.get(URL_BASE + '''forecast'''				,params=locals()							).json()
def      UpperCAmelCase__		(UpperCamelCase_ = 55.68				,UpperCamelCase_ = 12.57				,UpperCamelCase_ = APPID							):



     """simple docstring"""






     return requests.get(URL_BASE + '''onecall'''				,params=locals()							).json()


if __name__ == "__main__":
 from pprint import pprint

 while True:
  _SCREAMING_SNAKE_CASE     :					Any      =       input("Enter a location:").strip()
  if location:
   pprint(current_weather(location))
  else:
   break




